The Privacy/Utility Tradeoff
In the last lesson we learned why we use differential privacy to mitigate privacy attacks. In this lesson we'll dig deeper into the Laplace Mechanism with the goal of understanding how we can tune our random noise in such a way that individuals are protected and privacy is preserved.
Let's return to the Laplacian distribution
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While this captured the shape of the distribution, it is not the only laplace distribution. The width of the distribution can be expanded or contracted using the parameter ε. For the distribution above, we used ε=4. Use the slider below to visualize how the distribution changes as we change ε.
As you can see, raising epsilon shrinks the width of the distribution which increases the chances that a random number draw would select the value 0. In privacy terms a higher epsilon means less privacy. The tradeoff between privacy and utility is a tricky one.
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